International Journal of Latest Engineering Research and Applications (IJLERA) ISSN: 2455-7137
Volume – 01, Issue – 05, August – 2016, PP – 34-42
Detection of Unhealthy Region of plant leaves using Neural
Network
Lakhvir Kaur1, Dr. Vijay Laxmi2
¹Research Scholar M-Tech, Computer Science and Engineering, Guru Kashi University, India
²Dean, UCCA, Guru Kashi University, India
Abstract: A leaf is an organ of vascular plant and is the principal lateral appendage of the stem. Each leaf has a
set of features that differentiate it from the other leaves, such as margin and shape. This work proposes a
comparison of supervised plant leaves classification using different approaches, based on different
representations of these leaves, and the chosen algorithm. Beginning with the representation of leaves, we
presented leaves by a fine-scale margin feature histogram, by a Centroid Contour Distance Curve shape
signature, or by an interior texture feature histogram in 64 element vector for each one, after we tried different
combination among these features to optimize results. We classified the obtained vectors. Then we evaluate the
classification using cross validation. The obtained results are very interesting and show the importance of each
feature. The classification of plant leaf images with biometric features. Traditionally, the trained taxonomic
perform this process by following various tasks. The taxonomic usually classify the plants based on flowering
and associative phenomenon. It was found that this process was time consuming and difficult. The biometric
features of plants leaf like venation make this classification easy. Leaf biometric feature are analyzed using
computer based method like morphological feature analysis and artificial neural network and Naive bayes based
classifier. KNN classification model take input as the leaf venation morphological feature and classify them into
four different species. The result of this classification based on leaf venation is achieved 96.53% accuracy in the
training of the model for classification of leaves provide the 91% accuracy in testing to classify the leaf images.
Keyword: Artificial neural network, K-NN Classification, Leaf venation pattern, Morphological Features
I.
INTRODUCTION
Plant diseases have turned into a dilemma as it can cause significant reduction in both quality and
quantity of agricultural products. In India 70% of the population depend on agriculture. Farmers have wide
range of diversity to select suitable Fruit and Vegetable crops. However, the cultivation of these crops for
optimum yield and quality produce is highly technical. It can be improved by the aid of technological support.
The management of perennial fruit crops requires close monitoring especially for the management of diseases
that can affect production significantly and subsequently the postharvest life. In [2] the authors have worked on
the development of methods for the automatic classification of leaf diseases based on high resolution
multispectral and stereo images. Leaves of sugar beet are used for evaluating their approach. Sugar beet leaves
might be infected by several diseases, such as rusts (Uromyces betae), powdery mildew (Erysiphe betae).
Disease is caused by pathogen which is any agent causing disease. In most of the cases pests or diseases are seen
on the leaves or stems of the plant. Therefore identification of plants, leaves, stems and finding out the pest or
diseases, percentage of the pest or disease incidence , symptoms of the pest or disease attack, plays a key role in
successful cultivation of crops. It is found that diseases cause heavy crop losses amounting to several billion
dollars annually.
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International Journal of Latest Engineering Research and Applications (IJLERA) ISSN: 2455-7137
Volume – 01, Issue – 05, August – 2016, PP – 34-42
Disease management is a challenging task. Mostly diseases are seen on the leaves or stems of the plant. Precise
quantification of these visually observed diseases, pests, traits has not studied yet because of the complexity of
visual patterns. Hence there has been increasing demand for more specific and sophisticated image pattern
understanding. In biological science, sometimes thousands of images are generated in a single experiment.
These images can be required for further studies like classifying lesion, scoring quantitative traits, calculating
area eaten by insects, etc. Almost all of these tasks are processed manually or with distinct software packages. It
is not only tremendous amount of work but also suffers from two major issues: excessive processing time and
subjectiveness rising from different individuals .Hence to conduct high throughput experiments, plant biologist
need efficient computer software to automatically extract and analyze significant content. Here image
processing plays important role.
II.
BIOLOGY POINT OF VIEW
In leaves recognition research, a lot has been done about general features extraction or recognition
between different classes of objects. In case of specific domain recognition, taking into account the unique
characteristics that belong to this category, improves the performance of the system. Despite the high technical
aspect of this project, dealing with leaves gives a biological connotation. A very basic knowledge on leaves has
to be learned and knowing the perspective of how biologists themselves recognizing a leaf is and add on.
Biologists also emphasize the importance of leaves; indeed their size, their shape, their disposition can vary very
much and be a good mean for differentiating similar blooms. The disposition of the leaves on the stem can be
alternate, opposed or whorled as illustrated in Figure 1.3. The enervations of the leaf can be of different types;
there are leaves with dichotomist, parallel, palmate, pinnate nerves. These features are well explained below:
Figure 1.3: Compound leaves.
III.
LITERATURE SURVEY
Ana Carolina Quintao Siravenha (2015), Plant identification and classification play an important role in
ecology, but the manual process is cumbersome even for experimented taxonomists. In this work, a
methodology for plant identification and classification based on leaf shapes, that explores the discriminative
power of the contour-Centroid distance in the Fourier frequency domain in which some invariance (e.g. rotation
and scale) are guaranteed. In addition, it is also investigated the influence of feature selection techniques
regarding classification accuracy. Our results show that by combining a set of features vectors - in the principal
components space - and a feed forward neural network, an accuracy of 97.45% was achieved[7]. Francis Rey F.
Padao (2015), here research aims to study plant classification using naive Bayes (NB) method. Leaf shape and
texture serves as input features to the model classifier. The test result shows that the classification accuracy of
the model is high that the true positive rating is excellent and the weighted average of the false positive rating is
0.09%, which is considered very minimal and acceptable [14]. Ghulam Mustafa Choudhary(2015), There
author aimed to design & implement an image processing software based solution for automatic detection and
classification of plant leaf disease. Naked eyes gives result less and are time consuming, so a new strategy is
acquainted for detecting plants leaf diseases. It is very sensitive and accurate method in the detection of plant
diseases, which will diminish the losses and enhances the economical profit. In this author, describe two distinct
classes of plant diseases viz. scorch and spot are implemented for the detection of plant diseases. The main
characteristics of disease detection are speed and accuracy. Hence, there is working on development of
automatic, efficient, fast and accurate which is use for detection disease on unhealthy leaf [15]. Sachin D.
Khirade et al. (2015), the key is to prevent the losses in the yield and quantity of the agricultural product of the
plant diseases Identification. Visually observable patterns seen on the plant is the studies of the plant diseases.
Monitoring health and detection of disease on plant is very critical for sustainable agriculture. It is very difficult
to monitor the plant diseases manually. It requires tremendous amount of work, expertise in the plant diseases,
and also require the excessive processing time. Hence, image processing is used for the detection of plant
diseases. Here various methods are discussed which are used for the detection of plant diseases using their
leaves images with some segmentation and feature extraction algorithm that are used in the plant disease
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International Journal of Latest Engineering Research and Applications (IJLERA) ISSN: 2455-7137
Volume – 01, Issue – 05, August – 2016, PP – 34-42
detection [29]. Arti N. Rathod (2014), here author represents that in agriculture research of automatic leaf
disease detection is essential & benefits in monitoring large fields of crops, and thus automatically detect
symptoms of disease as soon as they appear on plant leaves. The term disease is usually used only for
destruction of live plants. Here, various methods studies are for increasing throughput and reduction
subjectiveness arising from human experts in detecting the leaf disease. The main characteristics of disease
detection are working on the development of automatic, efficient, fast and accurate detecting of unhealthy leaf
[9]. Khushal Khairnar (2014), here author aimed to decrease the diseases which restrict the growth of plant
and quality and quantity of plant also reduces. Image processing is best way for detecting and diagnosis the
diseases. In which initially the infected region is found then different features are extracted such as color, texture
and shape. Finally classification technique is used for detecting the diseases. Here explained the survey of all
detection procedures used for object detection [21]. Prof. Meeta Kumar et al. (2013), In this paper we present
survey on various classification techniques which can be used for plant leaf classification. A classification
problem deals with associating a given input pattern with one of the distinct classes. Plant leaf classification is a
technique where leaf is classified based on its different morphological features. There are various successful
classification techniques like k-Nearest Neighbor Classifier, Probabilistic Neural Network, Genetic Algorithm,
Support Vector Machine, and Principal Component Analysis. Deciding on the method for classification is often
a difficult task because the quality of the results can be different for different input data. Plant leaf
classifications has wide applications in various fields such as botany, Ayurveda, Agriculture etc. The goal of this
survey is to provide an overview of different classification techniques for plant leaf classification [24]. Smita
Naikwadi et.al. (2013) We propose and experimentally evaluate a software solution for automatic detection and
classification of plant leaf diseases. Studies of plant trait/disease refer to the studies of visually observable
patterns of a particular plant. Nowadays crops face many traits/diseases. Damage of the insect is one of the
major trait/disease. Insecticides are not always proved efficient because insecticides may be toxic to some kind
of birds. It also damages natural animal food chains. The following two steps are added successively after th
segmentation phase. In the first step we identify the mostly green colored pixels. Next, these pixels are masked
based on specific threshold values that are computed using Otsu's method, then those mostly green pixels are
masked. The other additional step is that the pixels with zeros red, green and blue values and the pixels on the
boundaries of the infected cluster (object) were completely removed. The experimental results demonstrate that
the proposed technique is a robust technique for the detection of plant leaves diseases. The developed
algorithm’s efficiency can successfully detect and classify the examined diseases with a precision between 83%
and 94%, and can achieve 20% speedup over the approach proposed in [30]. Abdul Kadir et.al. (2012) This
paper reports the results of experiments in improving performance of leaf identification system using Principal
Component Analysis (PCA). The system involved combination of features derived from shape, vein, color, and
texture of leaf. PCA was incorporated to the identification system to convert the features into orthogonal
features and then the results were inputted to the classifier that used Probabilistic Neural Network (PNN). This
approach has been tested on two datasets, Foliage and Flavia, that contain various color leaves (foliage plants)
and green leaves respectively. The results showed that PCA can increase the accuracy of the leaf identification
system on both datasets[1]. Gurpreet kaur et.al. (2012), Plants play an important role in human life and
provide required information for the development of human society. The urgent situation is that due to
environmental degradation and lack of awareness, many rare plant species are at the risk of extinction so it is
necessary to keep record for plant protection. We believe that the first step is to build up a database for
protecting plants. So the need arises to teach a computer how to classify plants. Despite the great advances made
in Botany, there are many plants which are still unknown. This research focuses on using digital image
processing for the purpose of automate classification and recognition of plants based on the images of the
leaves. In this paper we review leaf architecture and various techniques for automated plant classification and
recognition. [16]. Samuel E. Buttrey et.al. (2002) we construct a hybrid (composite) classifier by combining
two classifiers in common use— classification trees and k-nearest-neighbor (k-NN). In our scheme we divide
the feature space up by a classification tree, and then classify test set items using the k-NN rule just among those
training items in the same leaf as the test item. This reduces somewhat the computational load associated with kNN, and it produces a classification rule that performs better than either trees or the usual k-NN in a number of
well-known data sets [28].
IV.
METHDOLOGY
1. RGB image acquisition
2. Create the color transformation structure
3. Convert the color values in RGB to the space specified in the color transformation structure
4. Apply K-means clustering
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International Journal of Latest Engineering Research and Applications (IJLERA) ISSN: 2455-7137
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5. Masking green-pixels
6. Remove the masked cells inside the boundaries of the infected clusters
7. Convert the infected (cluster / clusters) from RGB to HSI Translation
8. SGDM Matrix Generation for H and S
9. Calling the GLCM function to calculate the features
10. Texture Statistics Computation
11. Configuring Neural Networks for Recognition
12. Grading and percentage uses Naïve Bayes system.
V.
RESULTS
The Chapter result and discussion incluse the different snapshort of the work . These snapshorts are given below
:
Figure 1.4: Input User interface
Figure 1.4, shows the output window as GUI part in which we select first leaf image by selecting Laod Image
button and then another buttons are there for performing functions to detect leafs disease.
Figure 1.5: Leaf Browsing Window for selecting Leaf
Figure 1.5, shows the window for browsing leaf image, after selecting button Load image.
Figure 1.6: Original image on User Interface
Above, figure 1.6, shows the original image of leaf which we have selected for detecting disease.
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International Journal of Latest Engineering Research and Applications (IJLERA) ISSN: 2455-7137
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Figure 1.7: Enhanced Image of Original on User Interface
Figure 1.7, shows the Enhanced image of original image after clicking on button Enhanced Contrast of original
image. Enhancing of an image is done for feature extraction.
Figure 1.8: K-mean Segmentation of Original image on User Interface
Above, figure 1.8, shows the output image as K-mean segmentation done with cluster classification up to 3 is
shown by selecting button segmentation. Here, actually images through K-mean i.e K-NN is used for clustering
the images into different cluster level and shows the images.
After that a small dialog box appears in which it will ask the number for classification with neural network and
Naïve Bayes to get the accuracy and result for disease classification or the area of leaf which is affected by
name of the diease is identified.
Figure 1.9: Clusterting number entered for identifying disease type and accuracy
Figure 1.9, shows the dialog box for entering clustering number after segmentation for getting out accuracy and
identified the disease type which had affected on leaf.
Figure 1.10: Result of clustering with segmented Image
Above Figure 1.10, the segmented through K-NN clustering as entered iin the dialog box shown in figure 1.9.
after segmention and feature clasification result in name of disease type and affected region is calculated. Figure
1.11, shows the name of disease in dialog box below.
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International Journal of Latest Engineering Research and Applications (IJLERA) ISSN: 2455-7137
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Figure 1.11: Dialog box showing name of disease type
Figure 1.12: Result of Classification of disease type and affected region
Figure 1.12 above shows the result of affected region of leaf part in percentage (%). And the name of disease
type from which the selected leaf is affected.
Figure1.13: Calculating accuracy through Neural Network.
Figure 1.13, above shows the accuraaacy rate of plant detection using neural network classifier with the iteration
of 500. Accuracy result will be display on when button named Accuracy in % is clicked. Result will be shown in
percentage(%) in figure 1.14 below.
Figure 5.14: Neural network Accuracy Result
Figure 1.14, below shows the graphical representation means through graph expaling about the Naive bayes
with fuzzy system as showing accuracy of leaf diseases in percentage and grading point. Naive bayes is also a
classifier which is used for testing the training datasets like neural network . there is difference only in their
approaching ways and how does the data is selectted and defined.
Figure 1.15: Naive Bayes Result of Accuracy
Figure 1.15, shows the grading and percentage of Accuracy that had infected the leaf and disease has been
affected on leaf. X-axis shows the percentage from 1 to 100 and at Y-axis shows the Grade of Disease 0 to 5.
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International Journal of Latest Engineering Research and Applications (IJLERA) ISSN: 2455-7137
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VI.
DISCUSSION
The above GUI part can be solved for any type of leaf which is infected by any type of disease. Leaves
are detected from virus which infected the leaf of plants. İn this, we use to firstly take an image from images
database of leaf, then feature extraction is done by enhancng the image of selceted leaf and binary calculation is
calculated. Segmentation is done on the binary means the enhanced image using different types of clustering
range 1 to 3. Then at last we use to find out classification of leaf disease types and its affected region of selected
leaf. At last we can save this image and gets percentage as accuarcy of affectec part through Neurl Network and
Naive Bayes classifiers.
Table 5.1: Result of Accuracy through our Proposed Methods as Comparison
Image or Disease Type
Name of Segmentation on Neural
Network Naive Bayes Accuracy of
2 type Clustering
Accuracy of affected part affected part
Alternaria Alternata
15.0112
10.9
Anthracnose
15.7195
41
Bacterial Blight
15.0061
49.4
Cercospora Leaf Spot
15.4235
37.4
Healthy Leaf
95.1613
47.4
VII.
CONCLUSION
Plants play an important role in our lives, without plants there will not be the existence of the ecology
of the earth. The large amount of leaf types now makes the human being in a front of some problems in the
specification of the use of plants, the first need to know the use of a plant is the identification of the plant leaf.
This work proposed a comparison of supervised classification of plant leaves, where we used to represent
species in seven different representations, using three features extracted from binary masks of these leaves: a
finescale margin feature histogram, by a Centroid Contour Distance Curve shape signature, and by an interior
texture feature histogram. Results were very interesting in a way that gives as clear ideas: In term of
representation: we can differentiate leaves by its margin better than shape or texture, but, experiments shown in
this study prove our idea: the more we combine these features, the more precise the difference between samples
is and that is what gives better results in classification. In term of classification: distance based algorithms give
the best result for plant leaves classification. So, we can conclude that these algorithms are the most suitable for
that task. On the other hand, the approach based on decision tree gives the worst results because of the
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overfitting problem. In general, a learning algorithm is said to overfit relative to a simpler one if it is more
accurate in fitting known data but less accurate in predicting new data. Use of the three features proved that
there is some information more important than other. We discovered that margin representation can affect
results more than the shape of the leaf. However, the combination of the three features gives the best result. In
our algorithm, an enhancement on different methods such as K th-Nearest Neighbor with Neural Network and
Naive Bayes are compared and hybrid in classification of image analysis to improve grading and accuracy of
result. So, the post-processing of leafs are the detection of visual defects in order to classify the infected part
depending on their appearance. Also we can detect the disease type from our model.
VIII.
FUTURE SCOPE
We can further study, in the research that can be extended on the different types of leaves and plants
variety. Even this work can be done on fruits and vegetables quality detecting. To solve this problem, we plan,
as future work, use of feature extraction algorithms, like PSO, to clean dataset and keep the important
information in order to optimize the obtained results and avoid overfitting problem posed by decision tree
algorithm. We plan also to use bio-inspired algorithms. They are part of a new research domain that is becoming
more important due to its results in different areas.
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